Instructions to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B")
model = AutoModelWithLMHead.from_pretrained("OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B

SGLang

How to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B with Docker Model Runner:
```
docker model run hf.co/OsamaMo/Arabic_Text-To-SQL_using_Qwen2.5-1.5B
```

Arabic_Text-To-SQL_using_Qwen2.5-1.5B / LLaMA-Factory /docker /docker-cuda /Dockerfile

OsamaMo

Training in progress, step 500

93adfea verified about 1 year ago

raw

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3.05 kB

	# Default use the NVIDIA official image with PyTorch 2.3.0
	# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html
	ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:24.02-py3
	FROM ${BASE_IMAGE}

	# Define environments
	ENV MAX_JOBS=4
	ENV FLASH_ATTENTION_FORCE_BUILD=TRUE
	ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

	# Define installation arguments
	ARG INSTALL_BNB=false
	ARG INSTALL_VLLM=false
	ARG INSTALL_DEEPSPEED=false
	ARG INSTALL_FLASHATTN=false
	ARG INSTALL_LIGER_KERNEL=false
	ARG INSTALL_HQQ=false
	ARG INSTALL_EETQ=false
	ARG PIP_INDEX=https://pypi.org/simple
	ARG HTTP_PROXY=

	# Set the working directory
	WORKDIR /app

	# Set http proxy
	RUN if [ -n "$HTTP_PROXY" ]; then \
	echo "Configuring proxy..."; \
	export http_proxy=$HTTP_PROXY; \
	export https_proxy=$HTTP_PROXY; \
	fi

	# Install the requirements
	COPY requirements.txt /app
	RUN pip config set global.index-url "$PIP_INDEX" && \
	pip config set global.extra-index-url "$PIP_INDEX" && \
	python -m pip install --upgrade pip && \
	if [ -n "$HTTP_PROXY" ]; then \
	python -m pip install --proxy=$HTTP_PROXY -r requirements.txt; \
	else \
	python -m pip install -r requirements.txt; \
	fi

	# Copy the rest of the application into the image
	COPY . /app

	# Install the LLaMA Factory
	RUN EXTRA_PACKAGES="metrics"; \
	if [ "$INSTALL_BNB" == "true" ]; then \
	EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
	fi; \
	if [ "$INSTALL_VLLM" == "true" ]; then \
	EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
	fi; \
	if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
	EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
	fi; \
	if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
	EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
	fi; \
	if [ "$INSTALL_HQQ" == "true" ]; then \
	EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
	fi; \
	if [ "$INSTALL_EETQ" == "true" ]; then \
	EXTRA_PACKAGES="${EXTRA_PACKAGES},eetq"; \
	fi; \
	if [ -n "$HTTP_PROXY" ]; then \
	pip install --proxy=$HTTP_PROXY -e ".[$EXTRA_PACKAGES]"; \
	else \
	pip install -e ".[$EXTRA_PACKAGES]"; \
	fi

	# Rebuild flash attention
	RUN pip uninstall -y transformer-engine flash-attn && \
	if [ "$INSTALL_FLASHATTN" == "true" ]; then \
	pip uninstall -y ninja && \
	if [ -n "$HTTP_PROXY" ]; then \
	pip install --proxy=$HTTP_PROXY ninja && \
	pip install --proxy=$HTTP_PROXY --no-cache-dir flash-attn --no-build-isolation; \
	else \
	pip install ninja && \
	pip install --no-cache-dir flash-attn --no-build-isolation; \
	fi; \
	fi


	# Unset http proxy
	RUN if [ -n "$HTTP_PROXY" ]; then \
	unset http_proxy; \
	unset https_proxy; \
	fi

	# Set up volumes
	VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]

	# Expose port 7860 for the LLaMA Board
	ENV GRADIO_SERVER_PORT 7860
	EXPOSE 7860

	# Expose port 8000 for the API service
	ENV API_PORT 8000
	EXPOSE 8000